class_docstr: Classification and tagging of 2M comments on news sites.
  
  This version of the CivilComments Dataset provides access to the primary
  seven labels that were annotated by crowd workers, the toxicity and other
  tags are a value between 0 and 1 indicating the fraction of annotators that
  assigned these attributes to the comment text.
  
  The other tags are only available for a fraction of the input examples. They
  are currently ignored for the main dataset; the CivilCommentsIdentities set
  includes those labels, but only consists of the subset of the data with them.
  The other attributes that were part of the original CivilComments release are
  included only in the raw data. See the Kaggle documentation for more details
  about the available features.
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code: def _info(self):
      mode = self.builder_config.mode
      citation = {
          "base": _CITATION,
          "identity": _CITATION,
          "covert": _COVERT_CITATION,
          "spans": _SPANS_CITATION,
          "context": _CONTEXT_CITATION,
      }[mode]
      features = {
          "text": tfds.features.Text(),
          "id": np.str_,
          "parent_text": tfds.features.Text(),
          "parent_id": np.int32,
          "article_id": np.int32,
          "publication_id": np.str_,
          "created_date": np.str_,
      }
      if mode == "spans":
          features["spans"] = tfds.features.Tensor(
              shape=(None,), dtype=np.int32
          )
          supervised_value = "spans"
      else:
          for label in _labels(mode):
              features[label] = np.float32
          supervised_value = "toxicity"
  
      return tfds.core.DatasetInfo(
          builder=self,
          description=_COMMON_DESCRIPTION,
          features=tfds.features.FeaturesDict(features),
          # The supervised_keys version is very impoverished.
          supervised_keys=("text", supervised_value),
          homepage="https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data",
          citation=citation,
      )
  
file_path: datasets/tensorflow_datasets/text/civil_comments.py
imports: from __future__ import annotations
  import ast
  import csv
  import os
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 1 for calling the CivilComments obj's underlying member method code with example usage: obj._info() and returns: tfds